Checking date: 16/10/2019

Course: 2019/2020

Mathematics for Data Science
Study: Master in Statistics for Data Science (345)

Coordinating teacher: TERAN VERGARA, FERNANDO DE

Department assigned to the subject: Department of Mathematics

Type: Compulsory
ECTS Credits: 3.0 ECTS


Competences and skills that will be acquired and learning results.
While there are many applied mathematics techniques and concepts that are useful (and used) in Data Science, this course focus on the basics of those based on linear algebra and calculus, as they underlie many of the most importants applications and algorithms: Matrix algebra, Matrix decompositions.
Description of contents: programme
1. Linear Systems 2. Vectors 3. Matrices 4. Diagonalization 5. Orthogonality 6. Symmetric Matrices
Learning activities and methodology
Theoretical classes (lectures) Practical problems that students must solve individually as homework Tutorials
Assessment System
  • % end-of-term-examination 100
  • % of continuous assessment (assigments, laboratory, practicals...) 0
Basic Bibliography
  • David C. Lay, Steven R. Lay, Judi J. McDonald. Linear Algebra and Its Applications. Pearson; 5 edition. 2016
Additional Bibliography
  • Gilbert Strang. LINEAR ALGEBRA and learning from Data. Wellesley Cambridge Press. 2019
  • W. Keith Nicholson. Linear Algebra with Applications. McGraw-Hill, 6th edition. 2009
Recursos electrónicosElectronic Resources *
(*) Access to some electronic resources may be restricted to members of the university community and require validation through Campus Global. If you try to connect from outside of the University you will need to set up a VPN

The course syllabus and the academic weekly planning may change due academic events or other reasons.